Latent Variable Mixture Modeling Approaches to Investigating Longitudinal Recovery Processes

doi:10.1017/9781108976213.012

8 - Latent Variable Mixture Modeling Approaches to Investigating Longitudinal Recovery Processes

from Part I - Micro Level

Published online by Cambridge University Press: 23 December 2021

JeeWon Cheong ,

Adam D. Wilson and

Juhan Lee

Edited by

Jalie A. Tucker and

Katie Witkiewitz

Show author details

Jalie A. Tucker: Affiliation:
University of Florida
Katie Witkiewitz: Affiliation:
University of New Mexico

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Summary

Alcohol researchers are often interested in identifying heterogeneous subgroups of drinkers, such as those with stable patterns of moderation drinking or patterns of heavy episodic drinking. Subgroups may also be identified based on qualitatively different developmental courses in the onset of alcohol use disorder (AUD) or pathways to recovery from AUD. This chapter provides an overview of latent variable mixture modeling, which can be useful for investigating such heterogeneity. First, mixture models applied to cross-sectional data are described, specifically latent class analysis and latent profile analysis. Then conventional latent growth modeling is discussed as a special case of growth mixture models, where subgroups of individuals are identified based on the shape of their growth trajectories. Mixture models applied to mediation analysis are also discussed. The chapter concludes with some practical issues to consider when using mixture modeling.

Keywords

alcohol use disorder recovery mixture modeling person-centered approach latent class analysis latent profile analysis growth mixture modeling person-centered mediation analysis

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Type: Chapter
Information: Dynamic Pathways to Recovery from Alcohol Use Disorder
Meaning and Methods
, pp. 137 - 156

DOI: https://doi.org/10.1017/9781108976213.012 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2022

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